mlsanityData to 7 Oct 2026

ITRE: Low-light Image Enhancement Based on Illumination Transmission Ratio Estimation

arXiv 2310.05158 ↗First posted 8 Oct 2023cs.CV cs.MMcited by 1

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Abstract (arXiv)

Noise, artifacts, and over-exposure are significant challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a novel Retinex-based method, called ITRE, which suppresses noise and artifacts from the origin of the model, prevents over-exposure throughout the enhancement process. Specifically, we assume that there must exist a pixel which is least disturbed by low light within pixels of same color. First, clustering the pixels on the RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the whole image, which determines that noise is not over-amplified easily. Next, we consider ITR of the image as the initial illumination transmission map to construct a base model for refined transmission map, which prevents artifacts. Additionally, we design an over-exposure module that captures the fundamental characteristics of pixel over-exposure and seamlessly integrate it into the base model. Finally, there is a possibility of weak enhancement when inter-class distance of pixels with same color is too small. To counteract this, we design a Robust-Guard module that safeguards the robustness of the image enhancement process. Extensive experiments demonstrate the effectiveness of our approach in suppressing noise, preventing artifacts, and controlling over-exposure level simultaneously. Our method performs superiority in qualitative and quantitative performance evaluations by comparing with state-of-the-art methods.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 60 references that are in the map. First posted on arXiv 8 Oct 2023.

Builds on

10 of 60 references.

References · 10

  1. 2018Deep Retinex Decomposition for Low-Light Enhancementcited by 720
  2. 2019EnlightenGAN: Deep Light Enhancement without Paired Supervisioncited by 466
  3. 2020Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancementcited by 303
  4. 2021Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimationcited by 192
  5. 2023Low-Light Image Enhancement with Wavelet-based Diffusion Modelscited by 126
  6. 2021R2RNet: Low-light Image Enhancement via Real-low to Real-normal Networkcited by 106
  7. 2019UG2+ Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environmentscited by 84
  8. 2021Low-light Image Enhancement via Breaking Down the Darknesscited by 65
  9. 2021Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Visioncited by 22
  10. 2021SurroundNet: Towards Effective Low-Light Image Enhancementcited by 7

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